Hyperspectral imaging is a form of spectral imaging wherein information from across the electromagnetic spectrum is collected in many narrow spectral bands and processed. From the different spectral images that are collected, information of the objects that are imaged can be derived. For example, as certain objects leave unique spectral signatures in images which may even depend on the status of the object, information obtained by multi-spectral imaging can provide information regarding the presence and/or status of objects in a region that is imaged. After selection of a spectral range that will be imaged, as spectral images in this complete spectral range can be acquired, one does not need to have detailed prior knowledge of the objects, and post-processing may allow to obtain all available information. Whereas originally hyperspectral remote sensing was mainly used for mining and geology, other applications such as ecology, agriculture and surveillance also make use of the imaging technique.
Some agricultural and ecological applications are known wherein hyperspectral remote sensing is used, e.g. for monitoring the development and health of crops, grape variety detection, monitoring individual forest canopies, detection of the chemical composition of plants as well as early detection of disease outbreaks, monitoring of impact of pollution and other environmental factors, etc. are some of the agricultural applications of interest. Hyperspectral imaging also is used for studies of inland and coastal waters for detecting biophysical properties. In mineralogy, detection of valuable minerals such as gold or diamonds can be performed using hyperspectral sensing, but also detection of oil and gas leakage from pipelines and natural wells are envisaged. Detection of soil composition on earth or even at other planets, asteroids or comets also are possible applications of hyperspectral imaging. In surveillance, hyperspectral imaging can for example be performed for detection of living creatures.
In some applications, multi-spectral data can be obtained by collecting a full two dimensional image of a region in one spectral range of interest and by subsequently collecting other full two dimensional images of that region in other spectral ranges of interest whereby spectral filters are switched in between. This way of data collection nevertheless is not always possible, especially when the region of interest and the imaging system undergo a large relative movement with respect to each other.
In view of the relative movement, accurate determination of positional information is important for a correct interpretation of the collected different spectral data. Known systems make use of a global positioning system (GPS) and/or an inertial measurement unit (IMU).
International patent application publication WO 2011/073430 A1, in the name of the present applicant, discloses a sensing device for obtaining geometric referenced multi-spectral image data of a region of interest in relative movement with respect to the sensing device. The sensing device comprises a first two dimensional sensor element. The sensing device is adapted for obtaining subsequent multi-spectral images during said relative motion of the region of interest with respect to the sensing device thus providing spectrally distinct information for different parts of a region of interest using different parts of the first sensor. The sensing device also comprises a second two dimensional sensor element and is adapted for providing, using the second sensor element, an image of the region of interest for generating geometric referencing information to be coupled to the distinct spectral information.
The known sensor device acquires spectral data (with the first sensor element) and geometric data (with the second sensor element) at the same frame rate, e.g. 50 frames per second.
When the frame rate is further increased, the known sensor device generates a large amount of data which can be difficult to handle and the registration of the spectral data with the geometric data becomes computationally more demanding.
This disadvantage can render the known sensor device less suitable for applications which require a large number of spectral channels. Then a very high frame rate is required to ensure full spatial coverage in all the relevant bands of the spectrum.